The application of the compressed sensing (CS) method in the radar field enables the radar imaging system to satisfy both low data cost and high reconstruction quality; however, it is accompanied by enormous iterative operations and difficult adjustments of parameters. In this article, we propose a learning-based split unfolding framework, dubbed as split iterative sparse reconstruction network (SISR-Net), for near-field 3-D millimeter-wave (mmW) radar sparse imaging. First, a split iterative sparse reconstruction algorithm, i.e., SISRA, is proposed to theoretically guide the structure of the imaging framework. Subsequently, by combining the model-based CS method and data-driven deep learning method, SISR-Net is constructed by SISRA to produce 3-D mmW radar images efficiently with excellent explainability and generalization ability. Combining the radar-imaging kernel, echo-generation kernel, and the split Bregman method, the efficiency and stability of SISR-Net are guaranteed, and all the parameters are layer-varied and learned steadily by end-to-end training to improve the convergence and robustness of the imaging network. Simulated data and echo from a high-resolution mmW radar dataset 3DRIED are used to train and test the SISR-Net based on the Adam optimizer. For both simulation and extensive 3-D mmW radar-measured experiments, the proposed SISR-Net outperforms other state-of-the-art imaging methods in terms of imaging accuracy and generalization ability.

中文翻译：

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用于 3-D 毫米波雷达稀疏成像的基于学习的拆分展开框架

压缩感知（CS）方法在雷达领域的应用，使雷达成像系统能够同时满足低数据成本和高重建质量；但是，它伴随着巨大的迭代操作和参数的困难调整。在本文中，我们提出了一种基于学习的拆分展开框架，称为拆分迭代稀疏重建网络 (SISR-Net)，用于近场 3-D 毫米波 (mmW) 雷达稀疏成像。首先，提出一种分裂迭代稀疏重建算法SISRA，从理论上指导成像框架的结构。随后，通过结合基于模型的 CS 方法和数据驱动的深度学习方法，SISR-Net 由 SISRA 构建，以高效地生成 3-D mmW 雷达图像，具有出色的可解释性和泛化能力。结合雷达成像核、回波生成核和分裂Bregman方法，保证了SISR-Net的效率和稳定性，所有参数都是层变的，并且通过端到端训练稳定学习，以提高成像网络的收敛性和鲁棒性。来自高分辨率毫米波雷达数据集 3DRIED 的模拟数据和回波用于训练和测试基于 Adam 优化器的 SISR-Net。对于模拟和广泛的 3-D mmW 雷达测量实验，所提出的 SISR-Net 在成像精度和泛化能力方面优于其他最先进的成像方法。并且所有参数都是层变的，通过端到端的训练稳定地学习，以提高成像网络的收敛性和鲁棒性。来自高分辨率毫米波雷达数据集 3DRIED 的模拟数据和回波用于训练和测试基于 Adam 优化器的 SISR-Net。对于模拟和广泛的 3-D mmW 雷达测量实验，所提出的 SISR-Net 在成像精度和泛化能力方面优于其他最先进的成像方法。并且所有参数都是层变的，通过端到端的训练稳定地学习，以提高成像网络的收敛性和鲁棒性。来自高分辨率毫米波雷达数据集 3DRIED 的模拟数据和回波用于训练和测试基于 Adam 优化器的 SISR-Net。对于模拟和广泛的 3-D mmW 雷达测量实验，所提出的 SISR-Net 在成像精度和泛化能力方面优于其他最先进的成像方法。